1. Field of the Invention
This invention relates to an apparatus for storing information representing factors for a multi-layer neural network, which represent weights of connections between a plurality of neurons constituting a multi-layer neural network provided with a learning function by a back propagation method.
2. Description of the Prior Art
Various intellectual data bases have heretofore been used in various fields. The intellectual data bases receive many pieces of information, analyze the information, and feed out the results of analyses of the information.
Recently, a method for utilizing a neural network has been proposed, which can be utilized as an intellectual data base and which is quite different from the intellectual data base described above.
The neural network is provided with a learning function by back propagation method. Specifically, when information (an instructor signal), which represents whether an output signal obtained when an input signal is given is or is not correct, is fed into the neural network, the weights of connections between units in the neural network (i.e. the weights of synapse connections) are corrected. By repeating the learning operations of the neural network, the probability that a correct answer will be obtained in response to a new input signal can be kept high. (Such functions are described in, for example, "Learning representations by back-propagating errors" by D. E. Rumelhart, G. E. Hinton and R. J. Williams, Nature, 323-9,533-356, 1986a; "Back-propagation" by Hideki Aso, Computrol, No. 24, pp. 53-60; and "Neural Computer" by Kazuyuki Aihara, the publishing bureau of Tokyo Denki University).
As described above, by feeding a large number of input signals into a neural network, the results of judgments can be obtained on the basis of the input signals. For such purposes, it is necessary to employ a neural network, which is provided with a large number of neurons and constituted of a large number of layers.
However, when the number of neurons increases, the number of connections between the neurons becomes very large. Therefore, a storage device having a very large storage capacity must be employed to store the information representing the weights of the connections.
By way of example, a neural network may be constituted such that it may receive image signal components of an image signal representing picture elements in an X-ray image of a human body, which is an object, and may feed out characteristic measures of the X-ray image, such as the portion of the object (e.g. the head, the neck, the chest, or the abdomen) the image of which was recorded, and the mode used when the X-ray image was recorded (e.g. an ordinary image recording mode, a tomographic image recording mode, or an enlarged image recording mode). The neural network may be constituted of three layers; i.e. an input layer, an intermediate layer, and an output layer. The input layer may be composed of 256 (=16.times.16) units (i.e. the number of picture elements in the X-ray image is equal to 16.times.16). The intermediate layer may be composed of 16 neurons, and the output layer may be composed of two neurons. Such a neural network may be employed for each of 100 characteristic measures. Also, the information representing the weight factor of each connection between the neurons may be expressed with eight bytes. In such cases, in order to store the information representing the weight factors of all connections, the storage device must have a storage capacity calculated from the formula EQU (256.times.16+16.times.2).times.100.times.8=3 megabytes
Thus it is necessary for a storage device having a very large capacity to be used.